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Hisao Ishibuchi: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Satoshi Yokoyama, Naoki Namikawa, Tomoharu Nakashima, Masayo Udo, Hisao Ishibuchi
    Developing a Goal Keeper for Simulated RoboCup Soccer and its Performance Evaluation. [Citation Graph (0, 0)][DBLP]
    AMiRE, 2005, pp:75-80 [Conf]
  2. Hisao Ishibuchi
    Effects of Crossover Operations on the Performance of EMO Algorithms. [Citation Graph (0, 0)][DBLP]
    Practical Approaches to Multi-Objective Optimization, 2005, pp:- [Conf]
  3. Hisao Ishibuchi, Shiori Kaige, Kaname Narukawa
    Comparison Between Lamarckian and Baldwinian Repair on Multiobjective 0/1 Knapsack Problems. [Citation Graph (0, 0)][DBLP]
    EMO, 2005, pp:370-385 [Conf]
  4. Hisao Ishibuchi, Kaname Narukawa
    Recombination of Similar Parents in EMO Algorithms. [Citation Graph (0, 0)][DBLP]
    EMO, 2005, pp:265-279 [Conf]
  5. Hisao Ishibuchi, Tomoharu Nakashima, Tadahiko Murata
    Multiobjective Optimization in Linguistic Rule Extraction from Numerical Data. [Citation Graph (0, 0)][DBLP]
    EMO, 2001, pp:588-602 [Conf]
  6. Hisao Ishibuchi, Youhei Shibata
    An Empirical Study on the Effect of Mating Restriction on the Search Ability of EMO Algorithms. [Citation Graph (0, 0)][DBLP]
    EMO, 2003, pp:433-447 [Conf]
  7. Hisao Ishibuchi, Takashi Yamamoto
    Effects of Three-Objective Genetic Rule Selection on the Generalization Ability of Fuzzy Rule-Based Systems. [Citation Graph (0, 0)][DBLP]
    EMO, 2003, pp:608-622 [Conf]
  8. Tadahiko Murata, Hisao Ishibuchi, Mitsuo Gen
    Specification of Genetic Search Directions in Cellular Multi-objective Genetic Algorithms. [Citation Graph (0, 0)][DBLP]
    EMO, 2001, pp:82-95 [Conf]
  9. Tadahiko Murata, Hiroyuki Nozawa, Hisao Ishibuchi, Mitsuo Gen
    Modification of Local Search Directions for Non-dominated Solutions in CellularMultiobjective Genetic Algorithms forPattern Classification Problems. [Citation Graph (0, 0)][DBLP]
    EMO, 2003, pp:593-607 [Conf]
  10. Yusuke Nojima, Kaname Narukawa, Shiori Kaige, Hisao Ishibuchi
    Effects of Removing Overlapping Solutions on the Performance of the NSGA-II Algorithm. [Citation Graph (0, 0)][DBLP]
    EMO, 2005, pp:341-354 [Conf]
  11. Hisao Ishibuchi, Yusuke Nojima
    Optimization of Scalarizing Functions Through Evolutionary Multiobjective Optimization. [Citation Graph (0, 0)][DBLP]
    EMO, 2006, pp:51-65 [Conf]
  12. Tadahiko Murata, Hisao Ishibuchi, Tomoharu Nakashima, Mitsuo Gen
    Fuzzy Partition and Input Selection by Genetic Algorithms for Designing Fuzzy Rule-Based Classification Systems. [Citation Graph (0, 0)][DBLP]
    Evolutionary Programming, 1998, pp:407-416 [Conf]
  13. Tomoharu Nakashima, Takanobu Ariyama, Hisao Ishibuchi
    On-Line Learning of a Fuzzy System for a Future Market. [Citation Graph (0, 0)][DBLP]
    FSKD, 2002, pp:54-58 [Conf]
  14. Tomoharu Nakashima, Gaku Nakai, Hisao Ishibuchi
    A Boosting Algorithm of Fuzzy Rule-Based Systems for Pattern Classification Problems. [Citation Graph (0, 0)][DBLP]
    FSKD, 2002, pp:155-158 [Conf]
  15. Hisao Ishibuchi, Ryoji Sakamoto, Tomoharu Nakashima
    Adaption of Fuzzy Rule-Based Systems for Game Playing . [Citation Graph (0, 0)][DBLP]
    FUZZ-IEEE, 2001, pp:1448-1451 [Conf]
  16. Hisao Ishibuchi, Takashi Yamamoto, Tomoharu Nakashima
    Linguistic Modelling for Function Approximation Using Grid Partitions. [Citation Graph (0, 0)][DBLP]
    FUZZ-IEEE, 2001, pp:47-50 [Conf]
  17. Hisao Ishibuchi, Takashi Yamamoto, Tomoharu Nakashima
    Determination of Rule Weights of Fuzzy Association Rules. [Citation Graph (0, 0)][DBLP]
    FUZZ-IEEE, 2001, pp:1555-1558 [Conf]
  18. Hisao Ishibuchi, Tomoharu Nakashima
    Linguistic Rule Extraction by Genetics-Based Machine Learning. [Citation Graph (0, 0)][DBLP]
    GECCO, 2000, pp:195-202 [Conf]
  19. Hisao Ishibuchi, Tomoharu Nakashima
    Multi-objective pattern and feature selection by a genetic algorithm. [Citation Graph (0, 0)][DBLP]
    GECCO, 2000, pp:1069-0 [Conf]
  20. Hisao Ishibuchi, Kaname Narukawa
    Some Issues on the Implementation of Local Search in Evolutionary Multiobjective Optimization. [Citation Graph (0, 0)][DBLP]
    GECCO (1), 2004, pp:1246-1258 [Conf]
  21. Hisao Ishibuchi, Kaname Narukawa
    Comparison of evolutionary multiobjective optimization with rference solution-based single-objective approach. [Citation Graph (0, 0)][DBLP]
    GECCO, 2005, pp:787-794 [Conf]
  22. Hisao Ishibuchi, Yusuke Nojima, Isao Kuwajima
    Multiobjective genetic rule selection as a data mining postprocessing procedure. [Citation Graph (0, 0)][DBLP]
    GECCO, 2006, pp:1591-1592 [Conf]
  23. Hisao Ishibuchi, Tatsuo Nakari, Tomoharu Nakashima
    Evolution of Strategies in Spatial IPD Games with Structure Demes. [Citation Graph (0, 0)][DBLP]
    GECCO, 2000, pp:817-824 [Conf]
  24. Hisao Ishibuchi, Kaname Narukawa, Yusuke Nojima
    An empirical study on the handling of overlapping solutions in evolutionary multiobjective optimization. [Citation Graph (0, 0)][DBLP]
    GECCO, 2005, pp:817-824 [Conf]
  25. Hisao Ishibuchi, Yusuke Nojima, Kaname Narukawa, Tsutomu Doi
    Incorporation of decision maker's preference into evolutionary multiobjective optimization algorithms. [Citation Graph (0, 0)][DBLP]
    GECCO, 2006, pp:741-742 [Conf]
  26. Hisao Ishibuchi, Youhei Shibata
    A Similarity-Based Mating Scheme for Evolutionary Multiobjective Optimization. [Citation Graph (0, 0)][DBLP]
    GECCO, 2003, pp:1065-1076 [Conf]
  27. Hisao Ishibuchi, Youhei Shibata
    Mating Scheme for Controlling the Diversity-Convergence Balance for Multiobjective Optimization. [Citation Graph (0, 0)][DBLP]
    GECCO (1), 2004, pp:1259-1271 [Conf]
  28. Hisao Ishibuchi, Takashi Yamamoto
    Fuzzy Rule Selection By Data Mining Criteria And Genetic Algorithms. [Citation Graph (0, 0)][DBLP]
    GECCO, 2002, pp:399-406 [Conf]
  29. Hisao Ishibuchi, Takashi Yamamoto
    Evolutionary Multiobjective Optimization for Generating an Ensemble of Fuzzy Rule-Based Classifiers. [Citation Graph (0, 0)][DBLP]
    GECCO, 2003, pp:1077-1088 [Conf]
  30. Hisao Ishibuchi, Tadashi Yoshida, Tadahiko Murata
    Balance Between Genetic Search And Local Search In Hybrid Evolutionary Multi-criterion Optimization Algorithms. [Citation Graph (0, 0)][DBLP]
    GECCO, 2002, pp:1301-1308 [Conf]
  31. Tadahiko Murata, Hisao Ishibuchi, Mitsuo Gen
    Cellular Genetic Local Search for Multi-Objective Optimization. [Citation Graph (0, 0)][DBLP]
    GECCO, 2000, pp:307-314 [Conf]
  32. Tadahiko Murata, Shiori Kaige, Hisao Ishibuchi
    Generalization of Dominance Relation-Based Replacement Rules for Memetic EMO Algorithms. [Citation Graph (0, 0)][DBLP]
    GECCO, 2003, pp:1234-1245 [Conf]
  33. Hisao Ishibuchi, Shiori Kaige
    A Simple but Powerful Multiobjective Hybrid Genetic Algorithm. [Citation Graph (0, 0)][DBLP]
    HIS, 2003, pp:244-251 [Conf]
  34. Hisao Ishibuchi, Kaname Narukawa
    Comparison of Local Search Implementation Schemes in Hybrid Evolutionary Multiobjective Optimization Algorithms. [Citation Graph (0, 0)][DBLP]
    HIS, 2004, pp:404-409 [Conf]
  35. Hisao Ishibuchi, Kaname Narukawa
    Spatial Implementation of Evolutionary Multiobjective Algorithms with Partial Lamarckian Repair for Multiobjective Knapsack Problems. [Citation Graph (0, 0)][DBLP]
    HIS, 2005, pp:265-270 [Conf]
  36. Hisao Ishibuchi, Yusuke Nojima
    Performance Evaluation of Evolutionary Multiobjective Approaches to the Design of Fuzzy Rule-Based Ensemble Classifiers. [Citation Graph (0, 0)][DBLP]
    HIS, 2005, pp:271-276 [Conf]
  37. Hisao Ishibuchi, Takashi Yamamoto
    Comparison of Fuzzy Rule Selection Criteria for Classification Problems. [Citation Graph (0, 0)][DBLP]
    HIS, 2002, pp:132-141 [Conf]
  38. Hisao Ishibuchi, Tadashi Yoshida
    Hybrid Evolutionary Multi-Objective Optimization Algorithms. [Citation Graph (0, 0)][DBLP]
    HIS, 2002, pp:163-172 [Conf]
  39. Yusuke Nojima, Hisao Ishibuchi
    Designing Fuzzy Ensemble Classifiers by Evolutionary Multiobjective Optimization with an Entropy-Based Diversity Criterion. [Citation Graph (0, 0)][DBLP]
    HIS, 2006, pp:59- [Conf]
  40. Hisao Ishibuchi, Takashi Yamamoto, Tomoharu Nakashima
    Fuzzy Data Mining: Effect of Fuzzy Discretization. [Citation Graph (0, 0)][DBLP]
    ICDM, 2001, pp:241-248 [Conf]
  41. Hisao Ishibuchi, Tadahiko Murata
    Multi-Objective Genetic Local Search Algorithm. [Citation Graph (0, 0)][DBLP]
    International Conference on Evolutionary Computation, 1996, pp:119-124 [Conf]
  42. Hisao Ishibuchi, Tomoharu Nakashima, Tadahiko Murata
    Genetic-Algorithm-Based Approaches to the Design of Fuzzy Systems for Multi-Dimensional Pattern Classification Problems. [Citation Graph (0, 0)][DBLP]
    International Conference on Evolutionary Computation, 1996, pp:229-234 [Conf]
  43. Tadahiko Murata, Hisao Ishibuchi
    Performance Evaluation of Genetic Algorithms for Flowshop Scheduling Problems. [Citation Graph (0, 0)][DBLP]
    International Conference on Evolutionary Computation, 1994, pp:812-817 [Conf]
  44. Tadahiko Murata, Hisao Ishibuchi
    Positive and Negative Combination Effects of Crossover and Mutation Operators in Sequencing Problems. [Citation Graph (0, 0)][DBLP]
    International Conference on Evolutionary Computation, 1996, pp:170-175 [Conf]
  45. Hisao Ishibuchi, Tadahiko Murata, Shigemitsu Tomioka
    Effectiveness of Genetic Local Search Algorithms. [Citation Graph (0, 0)][DBLP]
    ICGA, 1997, pp:505-512 [Conf]
  46. Hisao Ishibuchi, Yusuke Nojima, Isao Kuwajima
    Finding Simple Fuzzy Classification Systems with High Interpretability Through Multiobjective Rule Selection. [Citation Graph (0, 0)][DBLP]
    KES (2), 2006, pp:86-93 [Conf]
  47. Tomoharu Nakashima, Hisao Ishibuchi, Andrzej Bargiela
    A Study on Weighting Training Patterns for Fuzzy Rule-Based Classification Systems. [Citation Graph (0, 0)][DBLP]
    MDAI, 2004, pp:60-69 [Conf]
  48. Hisao Ishibuchi, Takashi Yamamoto
    Interpretability Issues in Fuzzy Genetics-Based Machine Learning for Linguistic Modelling. [Citation Graph (0, 0)][DBLP]
    Modelling with Words, 2003, pp:209-228 [Conf]
  49. Hisao Ishibuchi, Tomoharu Nakashima, Tadahiko Murata
    A Fuzzy Classifier System That Generates Linguistic Rules for Pattern Classification Problems. [Citation Graph (0, 0)][DBLP]
    IEEE/Nagoya-University World Wisepersons Workshop, 1995, pp:35-54 [Conf]
  50. Hisao Ishibuchi, Tsutomu Doi, Yusuke Nojima
    Incorporation of Scalarizing Fitness Functions into Evolutionary Multiobjective Optimization Algorithms. [Citation Graph (0, 0)][DBLP]
    PPSN, 2006, pp:493-502 [Conf]
  51. Hisao Ishibuchi, Tsutomu Doi, Yusuke Nojima
    Effects of Using Two Neighborhood Structures in Cellular Genetic Algorithms for Function Optimization. [Citation Graph (0, 0)][DBLP]
    PPSN, 2006, pp:949-958 [Conf]
  52. Hisao Ishibuchi, Satoshi Namba
    Evolutionary Multiobjective Knowledge Extraction for High-Dimensional Pattern Classification Problems. [Citation Graph (0, 0)][DBLP]
    PPSN, 2004, pp:1123-1132 [Conf]
  53. Tomoharu Nakashima, Masahiro Takatani, Masayo Udo, Hisao Ishibuchi, Manabu Nii
    Performance Evaluation of an Evolutionary Method for RoboCup Soccer Strategies. [Citation Graph (0, 0)][DBLP]
    RoboCup, 2005, pp:616-623 [Conf]
  54. Tomoharu Nakashima, Masayo Udo, Hisao Ishibuchi
    A Fuzzy Reinforcement Learning for a Ball Interception Problem. [Citation Graph (0, 0)][DBLP]
    RoboCup, 2003, pp:559-567 [Conf]
  55. Hisao Ishibuchi, Tomoharu Nakashima
    Evolution of Reference Sets in Nearest Neighbor Classification. [Citation Graph (0, 0)][DBLP]
    SEAL, 1998, pp:82-89 [Conf]
  56. Kimiko Tanaka, Manabu Nii, Hisao Ishibuchi
    Learning from Linguistic Rules and Rule Extraction for Function Approximation by Neural Networks. [Citation Graph (0, 0)][DBLP]
    SEAL, 1998, pp:317-324 [Conf]
  57. Hisao Ishibuchi, Takashi Yamamoto
    Multi-objective evolutionary design of fuzzy rule-based systems. [Citation Graph (0, 0)][DBLP]
    SMC (3), 2004, pp:2362-2367 [Conf]
  58. Tomoharu Nakashima, Hisao Ishibuchi, Andrzej Bargiela
    Constructing fuzzy classification systems from weighted training patterns. [Citation Graph (0, 0)][DBLP]
    SMC (3), 2004, pp:2386-2391 [Conf]
  59. Tomoharu Nakashima, Hiroko Kitano, Hisao Ishibuchi
    Development of a fuzzy position controller for an autonomously trading agent. [Citation Graph (0, 0)][DBLP]
    SMC (3), 2004, pp:2338-2343 [Conf]
  60. Tomoharu Nakashima, Masahiro Takatani, Masayo Udo, Hisao Ishibuchi
    An evolutionary approach for strategy learning in RoboCup soccer. [Citation Graph (0, 0)][DBLP]
    SMC (2), 2004, pp:2023-2028 [Conf]
  61. Tomoharu Nakashima, Hisao Ishibuchi, Masahiro Takatani, Manabu Nii
    The Effect of Using Match History on the Evolution of RoboCup Soccer Team Strategies. [Citation Graph (0, 0)][DBLP]
    CIG, 2006, pp:60-66 [Conf]
  62. Hisao Ishibuchi
    Book Review: "Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases" by Oscar Cordon, Francisco Herrera, Frank Hoffmann and Luis Magdalena; World Scientific, Singapore, New Jersey, London, Hong Kong, 2001, 462pp., ISBN 981-02-40 [Citation Graph (0, 0)][DBLP]
    Fuzzy Sets and Systems, 2004, v:141, n:1, pp:161-162 [Journal]
  63. Hisao Ishibuchi, Manabu Nii
    Numerical analysis of the learning of fuzzified neural networks from fuzzy if-then rules. [Citation Graph (0, 0)][DBLP]
    Fuzzy Sets and Systems, 2001, v:120, n:2, pp:281-307 [Journal]
  64. Hisao Ishibuchi, Manabu Nii
    Fuzzy regression using asymmetric fuzzy coefficients and fuzzified neural networks. [Citation Graph (0, 0)][DBLP]
    Fuzzy Sets and Systems, 2001, v:119, n:2, pp:273-290 [Journal]
  65. Hisao Ishibuchi, Ryoji Sakamoto, Tomoharu Nakashima
    Learning fuzzy rules from iterative execution of games. [Citation Graph (0, 0)][DBLP]
    Fuzzy Sets and Systems, 2003, v:135, n:2, pp:213-240 [Journal]
  66. Hisao Ishibuchi, Takashi Yamamoto
    Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. [Citation Graph (0, 0)][DBLP]
    Fuzzy Sets and Systems, 2004, v:141, n:1, pp:59-88 [Journal]
  67. Tomoharu Nakashima, Gerald Schaefer, Yasuyuki Yokota, Hisao Ishibuchi
    A weighted fuzzy classifier and its application to image processing tasks. [Citation Graph (0, 0)][DBLP]
    Fuzzy Sets and Systems, 2007, v:158, n:3, pp:284-294 [Journal]
  68. Hisao Ishibuchi
    Preface: 3rd international conference on fuzzy logic, neural nets, and soft computing. [Citation Graph (0, 0)][DBLP]
    Int. J. Approx. Reasoning, 1995, v:13, n:4, pp:247-248 [Journal]
  69. Hisao Ishibuchi, Kouichi Morioka, I. Burhan Türksen
    Learning by fuzzified neural networks. [Citation Graph (0, 0)][DBLP]
    Int. J. Approx. Reasoning, 1995, v:13, n:4, pp:327-358 [Journal]
  70. Hisao Ishibuchi, Hideo Tanaka, Hidehiko Okada
    Interpolation of fuzzy if-then rules by neural networks. [Citation Graph (0, 0)][DBLP]
    Int. J. Approx. Reasoning, 1994, v:10, n:1, pp:3-27 [Journal]
  71. Hideo Tanaka, Hisao Ishibuchi
    Evidence theory of exponential possibility distributions. [Citation Graph (0, 0)][DBLP]
    Int. J. Approx. Reasoning, 1993, v:8, n:2, pp:123-140 [Journal]
  72. Hisao Ishibuchi, Yusuke Nojima
    Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning. [Citation Graph (0, 0)][DBLP]
    Int. J. Approx. Reasoning, 2007, v:44, n:1, pp:4-31 [Journal]
  73. Hisao Ishibuchi, Shiori Kaige
    Implementation of Simple Multiobjective Memetic Algorithms and Its Applications to Knapsack Problems. [Citation Graph (0, 0)][DBLP]
    Int. J. Hybrid Intell. Syst., 2004, v:1, n:1, pp:22-35 [Journal]
  74. Hisao Ishibuchi, Yusuke Nojima
    Evolutionary multiobjective optimization for the design of fuzzy rule-based ensemble classifiers. [Citation Graph (0, 0)][DBLP]
    Int. J. Hybrid Intell. Syst., 2006, v:3, n:3, pp:129-145 [Journal]
  75. Hisao Ishibuchi, Tomoharu Nakashima, Tadahiko Murata
    Three-objective genetics-based machine learning for linguistic rule extraction. [Citation Graph (0, 0)][DBLP]
    Inf. Sci., 2001, v:136, n:1-4, pp:109-133 [Journal]
  76. Hisao Ishibuchi, Tadahiko Murata, Tomoharu Nakashima
    Linguistic Rule Extraction from Numerical Data for High-dimensional Classification Problems. [Citation Graph (0, 0)][DBLP]
    JACIII, 1999, v:3, n:5, pp:386-393 [Journal]
  77. Hisao Ishibuchi, Tomoharu Nakashima
    Pattern and Feature Selection by Genetic Algorithms in Nearest Neighbor Classification. [Citation Graph (0, 0)][DBLP]
    JACIII, 2000, v:4, n:2, pp:138-145 [Journal]
  78. Hisao Ishibuchi, Takashi Yamamoto, Tomoharu Nakashima
    An approach to fuzzy default reasoning for function approximation. [Citation Graph (0, 0)][DBLP]
    Soft Comput., 2006, v:10, n:9, pp:850-864 [Journal]
  79. Hisao Ishibuchi, Naoki Namikawa
    Evolution of iterated prisoner's dilemma game strategies in structured demes under random pairing in game playing. [Citation Graph (0, 0)][DBLP]
    IEEE Trans. Evolutionary Computation, 2005, v:9, n:6, pp:552-561 [Journal]
  80. Hisao Ishibuchi, Tomoharu Nakashima, Ryoji Sakamoto
    Evolution of unplanned coordination in a market selection game. [Citation Graph (0, 0)][DBLP]
    IEEE Trans. Evolutionary Computation, 2001, v:5, n:5, pp:524-534 [Journal]
  81. Hisao Ishibuchi, Tadashi Yoshida, Tadahiko Murata
    Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. [Citation Graph (0, 0)][DBLP]
    IEEE Trans. Evolutionary Computation, 2003, v:7, n:2, pp:204-223 [Journal]
  82. Hisao Ishibuchi, Takashi Yamamoto
    Rule Weight Specification in Fuzzy Rule-Based Classification Systems. [Citation Graph (0, 0)][DBLP]
    IEEE T. Fuzzy Systems, 2005, v:13, n:4, pp:428-435 [Journal]
  83. Hisao Ishibuchi, Tomoharu Nakashima, Tadahiko Murata
    Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Systems, Man, and Cybernetics, Part B, 1999, v:29, n:5, pp:601-618 [Journal]
  84. Hisao Ishibuchi, Takashi Yamamoto, Tomoharu Nakashima
    Hybridization of fuzzy GBML approaches for pattern classification problems. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Systems, Man, and Cybernetics, Part B, 2005, v:35, n:2, pp:359-365 [Journal]
  85. Hisao Ishibuchi, Yusuke Nojima, Noritaka Tsukamoto, Ken Ohara
    Effects of the use of non-geometric binary crossover on evolutionary multiobjective optimization. [Citation Graph (0, 0)][DBLP]
    GECCO, 2007, pp:829-836 [Conf]
  86. Hisao Ishibuchi, Isao Kuwajima, Yusuke Nojima
    Prescreening of Candidate Rules Using Association Rule Mining and Pareto-optimality in Genetic Rule Selection. [Citation Graph (0, 0)][DBLP]
    KES (2), 2007, pp:509-516 [Conf]
  87. Hisao Ishibuchi
    Evolutionary Multiobjective Design of Fuzzy Rule-Based Systems. [Citation Graph (0, 0)][DBLP]
    FOCI, 2007, pp:9-16 [Conf]
  88. Hisao Ishibuchi, Naoki Namikawa
    Evolution of cooperative behavior in the iterated prisoner's dilemma under random pairing in game playing. [Citation Graph (0, 0)][DBLP]
    Congress on Evolutionary Computation, 2005, pp:2637-2644 [Conf]
  89. Yusuke Nojima, Hisao Ishibuchi
    Genetic rule selection with a multi-classifier coding scheme for ensemble classifier design. [Citation Graph (0, 0)][DBLP]
    Int. J. Hybrid Intell. Syst., 2007, v:4, n:3, pp:157-169 [Journal]

  90. Cost-Sensitive Fuzzy Classification for Medical Diagnosis. [Citation Graph (, )][DBLP]


  91. Parallel Approaches for Multiobjective Optimization. [Citation Graph (, )][DBLP]


  92. Adaptation of Scalarizing Functions in MOEA/D: An Adaptive Scalarizing Function-Based Multiobjective Evolutionary Algorithm. [Citation Graph (, )][DBLP]


  93. Multiobjective Genetic Fuzzy Systems: Review and Future Research Directions. [Citation Graph (, )][DBLP]


  94. Breast Cancer Classification Using Statistical Features and Fuzzy Classification of Thermograms. [Citation Graph (, )][DBLP]


  95. Fuzzy Classification of Gene Expression Data. [Citation Graph (, )][DBLP]


  96. Introducing Class-Based Classification Priority in Fuzzy Rule-Based Classification Systems. [Citation Graph (, )][DBLP]


  97. Data Set Subdivision for Parallel Distributed Implementation of Genetic Fuzzy Rule Selection. [Citation Graph (, )][DBLP]


  98. Effectiveness of scalability improvement attempts on the performance of NSGA-II for many-objective problems. [Citation Graph (, )][DBLP]


  99. Maintaining the diversity of solutions by non-geometric binary crossover: a worst one-max solver competition case study. [Citation Graph (, )][DBLP]


  100. Single-objective and multi-objective formulations of solution selection for hypervolume maximization. [Citation Graph (, )][DBLP]


  101. Indicator-based evolutionary algorithm with hypervolume approximation by achievement scalarizing functions. [Citation Graph (, )][DBLP]


  102. Simultaneous use of different scalarizing functions in MOEA/D. [Citation Graph (, )][DBLP]


  103. Effects of Data Reduction on the Generalization Ability of Parallel Distributed Genetic Fuzzy Rule Selection. [Citation Graph (, )][DBLP]


  104. Neighborhood structures for genetic local search algorithms. [Citation Graph (, )][DBLP]


  105. Fuzzy arithmetic in neural networks for linguistic rule extraction. [Citation Graph (, )][DBLP]


  106. A study on generating fuzzy classification rules using histograms. [Citation Graph (, )][DBLP]


  107. Improving the generalization ability of neural networks by interval arithmetic. [Citation Graph (, )][DBLP]


  108. Examining the Effect of Elitism in Cellular Genetic Algorithms Using Two Neighborhood Structures. [Citation Graph (, )][DBLP]


  109. Use of Heuristic Local Search for Single-Objective Optimization in Multiobjective Memetic Algorithms. [Citation Graph (, )][DBLP]


  110. Many-Objective Test Problems to Visually Examine the Behavior of Multiobjective Evolution in a Decision Space. [Citation Graph (, )][DBLP]


  111. How to Choose Solutions for Local Search in Multiobjective Combinatorial Memetic Algorithms. [Citation Graph (, )][DBLP]


  112. Use of Local Ranking in Cellular Genetic Algorithms with Two Neighborhood Structures. [Citation Graph (, )][DBLP]


  113. Choosing extreme parents for diversity improvement in evolutionary multiobjective optimization algorithms. [Citation Graph (, )][DBLP]


  114. Evolutionary Many-Objective Optimization by NSGA-II and MOEA/D with Large Populations. [Citation Graph (, )][DBLP]


  115. Effects of spatial structures on evolution of iterated prisoner's dilemma game strategies with probabilistic decision making. [Citation Graph (, )][DBLP]


  116. Iterative approach to indicator-based multiobjective optimization. [Citation Graph (, )][DBLP]


  117. An empirical study on the specification of the local search application probability in multiobjective memetic algorithms. [Citation Graph (, )][DBLP]


  118. Scalability of multiobjective genetic local search to many-objective problems: Knapsack problem case studies. [Citation Graph (, )][DBLP]


  119. A study on constructing fuzzy systems for high-level decision making in a car racing game. [Citation Graph (, )][DBLP]


  120. Evolutionary many-objective optimization: A short review. [Citation Graph (, )][DBLP]


  121. Hypervolume approximation using achievement scalarizing functions for evolutionary many-objective optimization. [Citation Graph (, )][DBLP]


  122. Effects of using two neighborhood structures on the performance of cellular evolutionary algorithms for many-objective optimization. [Citation Graph (, )][DBLP]


  123. Learning Fuzzy If-Then Rules for Pattern Classi cation with Weighted Training Patterns. [Citation Graph (, )][DBLP]


  124. Multiobjective Formulations of Fuzzy Rule-Based Classification System Design. [Citation Graph (, )][DBLP]


  125. Discussions on Interpretability of Fuzzy Systems using Simple Examples. [Citation Graph (, )][DBLP]


  126. Interactive Fuzzy Modeling by Evolutionary Multiobjective Optimization with User Preference. [Citation Graph (, )][DBLP]


  127. Evolutionary multiobjective optimization and multiobjective fuzzy system design. [Citation Graph (, )][DBLP]


  128. Effects of Diversity Measures on the Design of Ensemble Classifiers by Multiobjective Genetic Fuzzy Rule Selection with a Multi-classifier Coding Scheme. [Citation Graph (, )][DBLP]


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